DeriveNet for (Very) Low Resolution Image Classification
Images captured from a distance often result in (very) low resolution (VLR/LR) region of interest, requiring automated identification. VLR/LR images (or regions of interest) often contain less information content, rendering ineffective feature extraction and classification. To this effect, this rese...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 10 vom: 11. Okt., Seite 6569-6577 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | Images captured from a distance often result in (very) low resolution (VLR/LR) region of interest, requiring automated identification. VLR/LR images (or regions of interest) often contain less information content, rendering ineffective feature extraction and classification. To this effect, this research proposes a novel DeriveNet model for VLR/LR classification, which focuses on learning effective class boundaries by utilizing the class-specific domain knowledge. DeriveNet model is jointly trained via two losses: (i) proposed Derived-Margin softmax loss and (ii) the proposed Reconstruction-Center (ReCent) loss. The Derived-Margin softmax loss focuses on learning an effective VLR classifier while explicitly modeling the inter-class variations. The ReCent loss incorporates domain information by learning a HR reconstruction space for approximating the class variations for the VLR/LR samples. It is utilized to derive inter-class margins for the Derived-Margin softmax loss. The DeriveNet model has been trained with a novel Multi-resolution Pyramid based data augmentation which enables the model to learn from varying resolutions during training. Experiments and analysis have been performed on multiple datasets for (i) VLR/LR face recognition, (ii) VLR digit classification, and (iii) VLR/LR face recognition from drone-shot videos. The DeriveNet model achieves state-of-the-art performance across different datasets, thus promoting its utility for several VLR/LR classification tasks |
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| Beschreibung: | Date Completed 16.09.2022 Date Revised 19.11.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2021.3088756 |